PEM-SMC: An algorithm for optimizing model parameters

Abstract

Bayesian inference is crucial for optimizing parameters in complex models, but often requires sampling due to high-dimensional, intractable posteriors. Beyond Markov-Chain Monte Carlo (MCMC) methods, Sequential Monte Carlo (SMC) algorithms offer an alternative. This paper introduces a Matlab toolbox for the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which combines the strengths of population-based MCMC and SMC. Two case studies – a complex multi-modal probability and a land surface model – demonstrate the toolbox’s capabilities. This tool is valuable for Bayesian inference across fields like statistics, ecology, hydrology, and land surface processes.